This book brings together chapters on the state-of-the-art in machine learning (ML) as it applies to the development of patient-centred technologies, with a special emphasis on “big data” and mobile data. With contributions from international experts from prestigious institutions it describes cutting edge research and makes accessible, for the first time, the latest in Bayesian non-parametrics for healthcare. This is one of the key frontiers in ML, and its application to healthcare will serve as a useful tutorial guide for both ML-focused and biomedical engineers. There are very few books that are accessible in this key area of ML, and absolutely none on the use of such technologies for mobile healthcare – despite a substantial amount of research that has taken place in this field at key biomedical and clinical sites across the world.
Topics covered include an introduction to machine learning in healthcare; discovering trends in patient physiology; Bayesian time-series analysis for patient monitoring; mobile healthcare for the developing world; massively-multiscale machine learning for healthcare; time-series clustering for understanding patient data; machine learning for home healthcare; fusing genomics and healthcare data; machine learning for mental health; mobile healthcare with analysis-on-a-chip; Bayesian analytics for medical data fusion.
This is an important book for academic and industrial researchers working in healthcare technologies, biomedical engineering and machine learning. It will also be of interest to advanced students working in these areas and commercial developers of computing-based healthcare applications.
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David Clifton is Associate Professor of Engineering Science at the University of Oxford, and a Research Fellow of the Royal Academy of Engineering. He leads the Computational Health Informatics Laboratory at the Institute of Biomedical Engineering in Oxford's Department of Engineering Science. Prof. Clifton's research focuses on the development of 'big data' machine learning for tracking the health of complex systems. He previously worked on the world's first FDA-approved multivariate patient monitoring system, and systems that are used to monitor 20,000 patients each month in the UK National Health Service.
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Hardback. Zustand: New. 1st. This book provides a snapshot of the state of current research at the interface between machine learning and healthcare with special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes. The book provides overviews on a range of technologies including detecting artefactual events in vital signs monitoring data; patient physiological monitoring; tracking infectious disease; predicting antibiotic resistance from genomic data; and managing chronic disease. With contributions from an international panel of leading researchers, this book will find a place on the bookshelves of academic and industrial researchers and advanced students working in healthcare technologies, biomedical engineering, and machine learning. Bestandsnummer des Verkäufers LU-9781849199780
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